ArticleChaos, solitons, and fractals2021
Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect.
Article in Chaos, solitons, and fractals, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Analysis of Drug-Resistant Bacteria Seasonality in Japan Using Financial Time Series Analysis Method: A Nationwide Longitudinal Study.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025Article
- Application of wastewater-based surveillance and copula time-series model for COVID-19 forecasts.The Science of the total environment · 2023Article
- Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement.Chaos, solitons, and fractals · 2022Article
- A comparison of mental arithmetic performance in time and frequency domains.Frontiers in psychology · 2022Article
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Authors and funding
1 author.
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Abstract
COVID-19 pandemic has affected more than a hundred fifty million people and killed over three million people worldwide over the past year. During this period, different forecasting models have tried to forecast time path of COVID-19 pandemic. Unlike the COVID-19 forecasting literature based on Autoregressive Integrated Moving Average (ARIMA) modelling, in this paper new COVID-19 cases were modelled and forecasted by conditional variance and asymmetric effects employing Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Threshold GARCH (TARCH) and Exponential GARCH (EGARCH) models. ARMA, ARMA-GARCH, ARMA-TGARCH and ARMA-EGARCH models were employed for one-day ahead forecasting performance for April, 2021 and three waves of COVID-19 pandemic in nine most affected countries -USA, India, Brazil, France, Russia, UK, Italy, Spain and Germany. Empirical results show that ARMA-GARCH models have better forecast performance than ARMA models by modelling both the conditional heteroskedasticity and the heavy-tailed distributions of the daily growth rate of the new confirmed cases; and asymmetric GARCH models show mixed results in terms of reducing the root mean squared error (RMSE).
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